EffcientNetB0-based Deep Learning Approach for Early Plant Disease Detection using Leaf Image Classification

Abstract

Plant diseases can damage crops and reduce their growth, which often results in economic problems in agriculture. Detecting these diseases at an early stage is essential to protect crop health and improve productivity. In this work, we developed an automated plant disease identification system using deep learning and leaf images from crops such as corn, grapes, and apples. Our introduced model is based on EfficientNetB0, which showed outstanding performance in classifying multiple disease categories. To make the system more reliable, data augmentation techniques were used to manage different lighting condition in lighting, angles, and backgrounds. The model obtained an accuracy of 97.92%, outperforming other architectures like InceptionV3, MobileNetV2, DenseNet121, Xception, and VGG16 with a lesser accuracy of 83.33%, 78.33%, 77.08%, 75%, 73.33% respectively. Performance measures like precision, recall, and F1-score confirmed its strong performance. The confusion matrix further showed that the model effectively distinguishes between healthy and diseased leaves. This approach provides a fast and accurate solution for real-time disease detection. Overall, the proposed system can support farmers in taking timely action and promoting sustainable agricultural practices.

Plant phenotyping relevance

葉画像から植物の病害状態を推定する深層学習手法を開発・比較評価しており、植物表現型の取得・分類が研究の中心であるため。

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